






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=__lowercase     )
class     lowerCamelCase (__lowercase     ):







		'''simple docstring'''



		_snake_case       :       Any					=    field(default='''language-modeling'''					,    metadata={'''include_in_asdict_even_if_is_default''': True}     )

		_snake_case       :       Optional[int]					=    Features({'''text''': Value('''string'''     )}     )
		_snake_case       :       int					=    Features({}     )
		_snake_case       :       Union[str, Any]					=    '''text'''



		@property
		def    __UpperCAmelCase     (				self  )      ->				Dict[str, str]:
							return {self.text_column: "text"}








import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)